Marketing Mix Model Guide With Dataset Using Python, R, and Excel

1.0 Introduction

What is the Marketing/Media Mix model?

According to Wikipedia, Marketing mix modeling (MMM) is a statistical analysis such as multivariate regressions on sales and marketing time series data to estimate the impact of various marketing tactics (marketing mix) on sales and then forecast the impact of future sets of tactics.

The key purpose: By estimating the effectiveness of different marketing channels activities, MMM helps to better understand how various marketing activities are driving the business metrics of a product and increase ROI.

What’s the difference between Marketing mix modeling (MMM) and Multi-touch attribution modeling(MTA)?

For offline media like TV, radio, or magazine, it is impossible to track individual impressions or clicks. The MMM model can use historical data to measure the total attributions of each offline channel with online channels.

In the digital world, the data is richer and most of the time, we are able to capture individual clicks and impression levels data.

In other words, MMM is an aggregate model while MTA is a user-level model.

What questions the MMM and MTA models can answer?

MMM model is often used to optimize advertising dollar amounts for the next quarter or year. Let the CMO and financial team make spending and allocation…